The emergence of industrial revolution 4.0, digital era, social media networks, ride-sharing transportation networks, video on demand, 5G network technology, and other cuttingedge technologies has transformed the current research paradigm in science into the fourth stage where "big data" is analyzed using various statistical explorations. Conversely, there are still deficiencies in scientific articles exploring "big data" in the logistics and supply chain research communities. Thus, the objective of this study was to conduct a comprehensive review of the trends and the current status of big data analytics (BDA) in the logistics and supply chain research communities. An elaborate examination was performed by analyzing the big data papers published in logistics and supply chain journals. The results have uncovered three things: the changing roles of BDA; some daunting challenges of implementing big data analytics, along with its recommended solutions; and the promising future of logistics and supply chains. All the findings would be helpful for creating a good research design and for implementing big data analytics (BDA) in the related fields.

Indonesia is facing many challenges in the fourth industrial revolution (4IR) era. One of them is related to big data technologies and implementation that can be seen clearly from Indonesia Industry Readiness Index (INI) 4.0. Therefore, focusing on implementing big data analytics in a gold, silver, and precious metal trading company is the objective of this manuscript to support daily business operations. To be more specific, the aim is to discover meaningful patterns and ensure high quality of knowledge discovery from the big data available in a company in Indonesia. It is needed to support the Making Indonesia 4.0 as a roadmap to implement industrial digitalization in Indonesia. The methodology used for the big data implementation in this manuscript is the combination of the CRISP-DM framework and key steps for customer analytics. The result of this research is a list of recommendations that facilitate strategic planning based on evidence of measurable big data analytics to achieve the business goals of a company.